Integration of sensor network and method of operation into a ctis framework

ABSTRACT

A rotary joint for use with a central tire inflation system and a network of sensorized components for a driveline is provided. The rotary joint comprises a sealing gasket wear detection system and a rotary encoder disposed within a joint cavity. The network of sensorized components comprises a sensorized wheel hub, a transmission speed sensor, and a controller in communication with the sensorized wheel hub and the transmission speed sensor through a communication bus. A method of utilizing a network of sensorized components to analyze a driveline and a method for monitoring a wear of a sealing gasket of a rotary joint is also provided.

RELATED APPLICATIONS

The present application claims the benefit of U.S. ProvisionalApplication No. 62/118,014 filed on Feb. 19, 2015, and is a divisionalof U.S. patent application Ser. No. 15/047,887 filed on Feb. 19, 2016,which are fully incorporated by reference herein.

FIELD OF THE INVENTION

The present invention relates to the use of sensorized components for adriveline and more particularly to sensorized components used with tireinflations systems and methods for estimating traction torque of thedriveline.

BACKGROUND OF THE INVENTION

Productivity of off-highway and heavy vehicles is typically correlatedto several factors, such as performance in a number of ways. Examples ofthese factors are drivability of the vehicle and reliability of thevehicle. Recently, vehicle manufacturers have expended great efforts toimprove performances of such vehicles, pushed by stringent customerrequirements and a high competitiveness of global markets. Modernengines and transmissions have recently integrated sensors and controlalgorithms that have significantly increased vehicle performance,allowing these vehicles to operate under critical conditions while stillbeing able to traverse large distances. Driveline components of suchvehicle may be complex subsystems that integrate auxiliary functions,which can enhance the usability and flexibility of off-highway vehicles.In particular, a vehicle may include an integrated central tireinflation system (CTIS) that provides for inflation or deflation of thetires of the vehicle through an on-board pump and a set of rotary jointsintegrated into wheel hubs. Wheel hubs used with a rotary joint of aCTIS are complex components from a mechanical point of view, but suchincreased complexity provides many advantages over conventional wheelhubs.

Wheel hubs are mechanical components in which movement and torque aretransferred from drive shafts into the axle to the wheel. Off-highwayvehicles are designed to work in tough conditions and therefore wheelhubs used with off-highway vehicle must also do the same. The componentsof the wheel hubs are enclosed to protect them from an externaloperating environment. In addition, the mechanical complexity of wheelhubs can increase even more when they include additionalfunctionalities, such as the forming a portion of a CTIS.

CTIS are on-board vehicle systems that allow an operator of the vehicleto inflate and deflate tires to improve drivability and fuel consumptionof the vehicle in response to a wide range of driving surfaces. FIGS.1A, 1C, and 1E illustrate a state of a tire in three conditions thevehicle including a CTIS may traverse. FIGS. 1B, 1D, and 1F illustrate a“footprint” of the tire against a surface the vehicle including a CTISmay traverse. High tire pressures, shown in FIG. 1A, are used on hardsurfaces, such as but not limited to tarmac, to reduce rolling friction,which decreases fuel consumption at higher speeds. Low tire pressures,shown in FIG. 1E, are used on loose surfaces where a large tirefootprint reduces a contact pressure, which improves vehicle grip anddrivability. Medium tire pressures, shown in FIG. 1C, are used inintermediary circumstances where good grip is required and where someamount of tire deflection is useful. The core component of the CTIS isthe rotary joint, which allows high pressure air flowing throughconduits forming a port of or placed adjacent the axle into the rotatingtire. Rotary joints in general comprise a first ring-shaped componentfixed to the axle or a steering arm, a second ring-shaped componentfixed to a rim of the wheel, and a series of sealing gaskets, typicallysealing lips, that retain high pressure air inside an interface volumebetween the two ring shaped components. FIGS. 2A and 2B schematicallyillustrate an exemplary embodiment of a wheel hub 200 including a rotaryjoint 202 known in the art. The rotary joint 202 shown in FIGS. 2A and2B further includes a dirt excluder 204, which is a gasket used toprotect the mechanical portions of the rotary joint 202 from undesireddust particles.

A wheel hub that allows for a monitoring of the sealing gaskets usedwithin while also allowing a torque delivered to the wheel to beestimated is not known in the art. Encoders integrated into wheel hubsto measure the rotational speed of the wheel, however, are known in theart. For example, U.S. Pat. No. 6,538,426 to Enrietto et al., disclosesa combined hub temperature and wheel speed sensor system.

It would be advantageous to develop a wheel hub for use with a centraltire inflation system for a vehicle including integrated sensors and amethod for processing information collected from such a wheel hub thatincreases a reliability of the wheel hub and provides information abouttorque delivered by the vehicle.

SUMMARY OF THE INVENTION

Presently provided by the invention, a wheel hub for use with a centraltire inflation system for a vehicle including integrated sensors and amethod for processing information collected from such a wheel hub thatincreases a reliability of the wheel hub and provides information abouttorque delivered by the vehicle, has surprisingly been discovered.

In one embodiment, the present invention is directed to a rotary jointfor use with a central tire inflation system. The rotary joint comprisesa non-rotating portion defining a portion of a conduit used with thecentral tire inflation system, a rotating portion defining a portion ofa conduit used with the central tire inflation system, the rotatingportion spaced apart from the non-rotating portion, a first sealinggasket disposed on one of the non-rotating portion and the rotatingportion and sealingly engaged with a remaining portion of a remainingone of the non-rotating portion and the rotating portion, the firstsealing gasket including a portion of a sealing gasket wear detectionsystem, a second sealing gasket disposed on one of the non-rotatingportion and the rotating portion and sealingly engaged with a remainingportion of a remaining one of the non-rotating portion and the rotatingportion, and a rotary encoder disposed within a joint cavity. A portionof the rotary encoder is disposed on the non-rotating portion and aremaining portion of the rotary encoder is disposed on the rotatingportion. The non-rotating portion, the rotating portion, the firstsealing gasket, and the second sealing gasket define the joint cavitywhich is a portion of a conduit used with the central tire inflationsystem.

In another embodiment, the present invention is directed to a network ofsensorized components for a driveline. The network comprises asensorized wheel hub in communication with a communication bus, atransmission speed sensor in communication with the communication bus,and a controller in communication with the sensorized wheel hub and thetransmission speed sensor through the communication bus. The controllerfacilitates determining at least one of an amount of torque applied toat least one wheel forming a portion of the driveline, an estimatedefficiency of a transmission of the driveline, an operational status ofthe sensorized wheel hub and the transmission speed sensor, a decreasedefficiency of the driveline, and a status of a portion of the drivelineusing signal analysis.

In yet another embodiment, the present invention is directed to a methodof utilizing a network of sensorized components to analyze a driveline.The method comprises the steps of providing a sensorized wheel hub incommunication with a communication bus, providing a transmission speedsensor in communication with the communication bus, providing acontroller in communication with the sensorized wheel hub and thetransmission speed sensor through the communication bus, collectinginformation from the sensorized wheel hub and the transmission speedsensor, fusing the collected information using the controller toreconstruct a current operational state of the driveline, comparing apreviously learned model of the driveline to the current operationalstate of the driveline, and determining if the current operational stateof the driveline is free from undesired disturbances, wherein in theevent that the driveline is free from undesired disturbances anestimation of the driveline torque is performed.

In yet another embodiment, the present invention is directed to a methodfor monitoring a wear of a sealing gasket of a rotary joint for use witha central tire inflation system. The method comprises the steps ofproviding the rotary joint comprising a non-rotating portion defining aportion of a conduit used with the central tire inflation system, arotating portion defining a portion of a conduit used with the centraltire inflation system, the rotating portion spaced apart from thenon-rotating portion, a first sealing gasket disposed on one of thenon-rotating portion and the rotating portion and sealingly engaged witha remaining portion of a remaining one of the non-rotating portion andthe rotating portion, and a second sealing gasket disposed on one of thenon-rotating portion and the rotating portion and sealingly engaged witha remaining portion of a remaining one of the non-rotating portion andthe rotating portion, providing a rotary encoder disposed within a jointcavity, wherein a portion of the rotary encoder is disposed on thenon-rotating portion and a remaining portion of the rotary encoder isdisposed on the rotating portion and the non-rotating portion, therotating portion, the first sealing gasket, and the second sealinggasket define a joint cavity which is a portion of a conduit used withthe central tire inflation system, providing a sealing gasket weardetection system comprising a sensor used within or adjacent the firstsealing gasket, a communication bus, and a controller, and using thecontroller to monitor for a signal indicating the first sealing gasketis in a worn condition, wherein the first sealing gasket and the rotaryencoder are in communication with the controller through thecommunication bus.

Various aspects of this invention will become apparent to those skilledin the art from the following detailed description of the preferredembodiment, when read in light of the accompanying drawings.

BRIEF DESCRIPTION OF THE FIGURES

The above, as well as other advantages of the present invention willbecome readily apparent to those skilled in the art from the followingdetailed description when considered in the light of the accompanyingdrawings in which:

FIG. 1A is a side plan view of a tire in a first condition;

FIG. 1B is a bottom plan view of a portion of the tire shown in FIG. 1Ashowing a “footprint” of the tire in the first condition against asurface;

FIG. 1C is a side plan view of a tire in a second condition;

FIG. 1D is a bottom plan view of a portion of the tire shown in FIG. 1Cshowing a “footprint” of the tire in the second condition against asurface;

FIG. 1E is a side plan view of a tire in a third condition;

FIG. 1F is a bottom plan view of a portion of the tire shown in FIG. 1Eshowing a “footprint” of the tire in the third condition against asurface;

FIGS. 2A and 2B schematically illustrate an exemplary embodiment of awheel hub including a rotary joint known in the art;

FIGS. 3 and 4 illustrate a wheel hub for use with a central tireinflation system according to an embodiment of the invention;

FIGS. 5A and 5B schematically illustrate an encoder and a ring-shapedmagnetized surface used with the rotary joint illustrated in FIGS. 3 and4;

FIG. 6A schematically illustrates a drivetrain for a vehicle, thedrivetrain including the rotary joint illustrated in FIGS. 3 and 4;

FIG. 6B illustrates a shaft depicting a methodology for determining anamount of torque based on torsional rigidity;

FIG. 6C schematically illustrates a filtering algorithm including asensor fusion and processing layer according to a method of theinvention;

FIG. 6D schematically illustrates a learning algorithm according to amethod of the invention;

FIG. 6E is a chart which illustrates a deterioration of an efficiency ofa driveline according to the invention with respect to a number ofoperating hours of the driveline;

FIG. 6F schematically illustrates an algorithm for identifying anomalouspatterns and updating a Learned Model of the Vehicle;

FIG. 6G schematically illustrates a classifier searches a measuredfeature vector for known patterns of a Learned Model of the Vehicle;

FIG. 7A illustrates a first configuration for positioning electrodesinto a sealing gasket used with the rotary joint illustrated in FIGS. 3and 4;

FIG. 7B illustrates an alternate embodiment of the configuration forpositioning electrodes into a sealing gasket illustrated in FIG. 7A;

FIG. 8A illustrates a second configuration for positioning electrodesinto a sealing gasket used with the rotary joint illustrated in FIGS. 3and 4;

FIG. 8B illustrates an alternate embodiment of the configuration forpositioning electrodes into a sealing gasket illustrated in FIG. 8A;

FIG. 9 is a detailed view of a magnetic sensor used with a rotary jointshown in FIG. 10A, the rotary joint including a magnetic sensor forsensing a magnetic field affected by a gap between a magnetic inclusionand a metallic surface of the rotary joint;

FIG. 10A illustrates a variation of the rotary joint shown in FIGS. 3and 4, the rotary joint including the magnetic sensor;

FIG. 10B is a detailed view of a magnetic sensor used with a rotaryjoint shown in FIG. 10A, the rotary joint including a magnetic sensorfor sensing a magnetic field affected by a gap between the magneticinclusion and the metallic surface of the rotary joint;

FIG. 11 illustrates the use of high pressure air within the rotary jointshown in FIGS. 3 and 4 to apply a sealing force to a pair of sealinggaskets;

FIG. 12 schematically illustrates a method for automatic calibration ofthe magnetic sensors shown in FIGS. 9 and 10; and

FIG. 13 is a chart indicating functionalities relating to the rotaryjoint shown in FIGS. 3 and 4 and the methodology for determining theamount of traction torque with respect to six variations of a network ofsensorized components for the driveline illustrated in FIG. 6A.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

It is to be understood that the invention may assume various alternativeorientations and step sequences, except where expressly specified to thecontrary. It is also to be understood that the specific devices andprocesses illustrated in the attached drawings, and described in thefollowing specification are simply exemplary embodiments of theinventive concepts of the present invention. Hence, specific dimensions,directions, orientations or other physical characteristics relating tothe embodiments disclosed are not to be considered as limiting, unlessexpressly stated otherwise.

FIGS. 3 and 4 illustrate a wheel hub 100 for use with a central tireinflation system (CTIS) (not shown). The CTIS including the wheel hub100 is a mechatronic system which integrates a network of sensors 102into portions of a rotary joint 103 used with a vehicle including theCTIS. The CTIS including the wheel hub 100 includes the use of signalprocessing algorithms and may find particular, usefulness withoff-highway vehicles. The CTIS including the wheel hub 100 also includesa methodology to acquire and pre-process data collected from each of thesensors 102. Pre-processing data from each of the sensors 102 preparesthe data for further use within the CTIS or in other vehicle systems.

The CTIS including the wheel hub 100 facilitates quantification of awear of at least one sealing gasket 104 used with the rotary joint 103.Further, the CTIS including the wheel hub 100 facilitates quantificationof a wear of a dirt excluder 105. The CTIS including the wheel hub 100also facilitates estimation of an amount of traction torque delivered toa wheel 106 associated with each wheel hub 100.

FIGS. 3 and 4 schematically illustrate an exemplary arrangement of aplurality of the sensors 102 within the rotary joint 103. A plurality ofconductors 108, each in communication with the sensors 102 and acommunication bus 110, are routed through a non-rotating portion 112 ofthe rotary joint 103. A rotating portion 114 of the rotary joint 103 isdisposed adjacent to the non-rotating portion 112. The communication bus110 is in further communication with a data logger (not shown).

A rotational speed of the wheels 106 is measured through an encoder 116(which is also one of the sensors 102) integrated into the rotary joint103. As a non-limiting example, the encoder 116 may be a magneticencoder, but it is understood that the encoder 116 may utilize othertechnologies, such as optical. For exemplary purposes, herein it isdescribed that the encoder 116 used with the rotary joint 103 is amagnetic encoder, but it is understood that the encoder 116 may be anytype of encoder used to establish a relationship between fixed androtating parts. A benefit of positioning the encoder 116 within therotary joint 103 is that it allows the encoder 116 to operate in aprotected and clean environment, since only air is flowing through therotary joint 103. FIGS. 5A and 5B schematically illustrate the encoder116 and a ring-shaped magnetized surface 118 used with the rotary joint103. The encoder 116 may be a Hall effect sensor which is coupled to thenon-rotating portion 112 of the rotary joint 103, and in communicationwith the data logger through the communication bus 110. Moreover, if theencoder 116 is a magnetic encoder utilizing a Hall effect sensor, theencoder 116 faces a periodic magnetic field generated by the ring-shapedmagnetized surface 118 (shown in FIG. 5B) on the rotating portion 114 ofthe rotary joint 103. When the wheel 106 rotates, the encoder 116measures a variation in a magnetic field induced by the ring-shapedmagnetized surface 118, and outputs a periodic electrical signal throughthe associated conductor 108. If an additional encoder (not shown),which may also be a Hall effect sensor, is placed in quadrature to theencoder 116, it is possible to determine a direction of rotation of thewheel 106. A resolution of the encoder 116 is selected according to arange of speeds to be measured. An upper limit of the measurable speedis determined by a frequency of the clock driving the counter, while aminimum measurable speed is determined by an angular resolution of theencoder 116.

The signal measured by the encoder 116 is processed through a countingalgorithm that is used in standard encoders, and provides a directmeasurement of a rotational speed of the wheel 106.

The CTIS including the wheel hub 100 uses sealing gaskets 104 fittedwith electrodes 120 or conductive material 122 to determine when wearoccurs to the sealing gasket 104. The dirt excluder 105 may also beconfigured in a similar manner described herein. FIGS. 7 and 8illustrate two possible configurations and two designs for eachconfiguration for positioning the electrodes 120 or conductive material122 into the sealing gaskets 104 to measure wear of the sealing gasket104.

As shown in FIG. 7A, the sealing gasket 104 is coupled to the rotatingportion 114 of the rotary joint 103. Two electrodes 120 are placedagainst an internal surface of the non-rotating portion 112 of therotary joint 103. A first one of the electrodes 120 is connected to aground and the remaining one of the electrodes 120 is connected to apull-up digital input of the data logger. A ring of the conductivematerial 122 is nested within the sealing gasket 104 and closes anelectrical circuit between the electrodes 120 when the sealing gasket104 becomes worn. It is understood that the electrodes 120 and theconductive material 122 nested within the sealing gasket 104 form asensor 102 for use with the rotary joint 103 as described hereinabove.

As shown in FIG. 8A, the sealing gasket 104 is fixed to the non-rotatingportion 112 of the rotary joint 103. Two electrodes 120 are integratedinto the sealing gasket 104. A first one of the electrodes 120 isconnected to a ground and the remaining one of the electrodes 120 isconnected to the pull-up digital input of the data logger. When thesealing gasket 104 becomes worn, the two electrodes 120 become exposedand close an electric circuit using the rotating portion 114, which actsas the conductive material 122. It is understood that the electrodes 120integrated into the sealing gasket 104 and the conductive material 122form a sensor 102 for use with the rotary joint 103 as describedhereinabove.

For each of the configurations shown in FIGS. 7A and 8A, alternateembodiments are shown in FIGS. 7B and 8B. Each of the designs shown inFIGS. 7B and 8B utilize a pair of semicircular electrodes 124 instead ofthe electrodes 120, which maximize a sensing area, and thus improves anaccuracy of the CTIS including the wheel hub 100.

FIGS. 9, 10A, and 10B illustrate a variation of the rotary joint 103shown in FIGS. 3 and 4. FIG. 10A illustrates a rotary joint 903 for usewith a central tire inflation system (CTIS) (not shown) including amagnetic sensor 930 for the proportional reading of the wear of thesealing gaskets 904. The embodiment shown in FIGS. 9, 10A, and 10Bincludes similar components to the rotary joint 103 shown in FIGS. 3 and4. Similar features of the embodiment shown in FIGS. 3 and 4 arenumbered similarly in series, with the exception of the featuresdescribed below.

The wheel hub 900 illustrated in FIG. 10A including the magnetic sensor930 provides proportional readings about a wear of the sealing gaskets904. As shown in FIGS. 9 and 10B, each of the sealing gaskets 904includes a metallic inclusion 932 located inside of the sealing gasket904. The magnetic sensor 930 is positioned adjacent to the sealinggasket 904. Each of the magnetic sensors 930 is configured to generateand sense a magnetic field 934. Such a magnetic field 934 is affected bya gap between the magnetic inclusion 932 and a metallic surface (forminga portion of the non-rotating portion 912 or the rotating portion 914)against which each of the sealing gaskets 904 is sealingly engaged with.As described herein, air pressure within the rotary joint 903facilitates sealing engagement between each of the sealing gaskets 904and the non-rotating portion 912 or the rotating portion 914.

The magnetic sensor 930 is preferably mounted on the non-rotatingportion 912 of the rotary joint 903, thus easing connections such aspower supply and channel outputs from each magnetic sensor 930. As shownin FIG. 9, when the sealing gasket 904 is anchored to the rotatingportion 914 of the rotary joint 903, the metallic inclusion 932 insideof the sealing gasket 904 can cover a small circumferential portion ofthe sealing gasket 904, or it can be a continuous ring. The sealinggasket 904 having the metallic inclusion 932 covering a smallcircumferential portion of the sealing gasket 904 is easier tomanufacture, however it provides only a local reading of the healthstatus of the sealing gasket 904 and it requires signal processinghardware (not shown) capable of measuring a periodic-impulsive signal.Alternately, in the preferred embodiment, the metallic inclusion 932 isthe continuous ring, which affords the opportunity to monitor the healthstatus of the whole sealing gasket 904.

When the sealing gasket 904 is anchored to the non-rotating portion 912of the rotary joint 903, as shown in FIG. 10B, the metallic inclusion932 can cover a small fraction of the circumference of the sealinggasket 904, since the magnetic sensor 930 will always be disposedadjacent the same portion of the sealing gasket 904. In such anembodiment, if it is desired to have a distributed monitoring of thehealth status of the sealing gasket 904, it is necessary to includeadditional magnetic sensors 930 within the rotary joint 903 which facedifferent angular sectors of the sealing gasket 904.

In addition to the rotary joint 903 as described hereinabove, theinvention also comprises a method for automatic calibration of themagnetic sensors 930 for monitoring wear of the sealing gasket 904. Acritical aspect of the magnetic sensor is represented by its Calibrationof the magnetic sensors 930 may be defined as the relation betweendeformation of the magnetic field 934 and a magnitude of a gap betweenthe metallic inclusion 932 and the opposite metallic surface of thenon-rotating portion 912 or the rotating portion 914 against which thesealing gasket 904 is sealingly engaged. Such a relation depends on manyfactors, for instance and as non-limiting examples, a wear of thesealing gasket 904, a pressure of the air inside of the rotary joint903, and speed of the rotating portion 914 of the rotary joint 903. Themethod for automatic calibration of the magnetic sensors 930 is based onmachine learning techniques, and the method is used to predict aresidual life of the sealing gasket 904. FIG. 12 schematicallyillustrates the method for automatic calibration of the magnetic sensors930.

For the sake of clarity, the method for automatic calibration of themagnetic sensors 930 as schematically illustrated in FIG. 12 is proposedin an exemplary manner, but it is understood that many other machinelearning approaches can be used (for example, deep learning, Gaussianprocesses, etc.). In a first step 1202, a plurality of measurements arecollected from the vehicle including a driveline 600 (shown in FIG. 6A)and CTIS system, such as but not limited to, a pressure in each of aplurality of wheels 608 (shown in FIG. 6A), a speed of the vehicle, anda deformation of the magnetic field 934 measured by the magnetic sensor930. Following measurement acquisition in the first step 1202, themethod includes four operation phases.

First, the overall functionality of each of the rotary joints 903 isverified at a first operation phase 1204. The first operation phase 1204is accomplished by performing periodic pressure checks. In the eventthat anomalous leakages are detected within the system a feedback to theuser is provided by raising a mounting error.

Next, a second operation phase 1206 lasts until a space s traveled bythe rotary joint 903 (calculated based on a rotational speed of therotary joint 903) is greater than a strain value (for example, thestrain value might be about 15-20% of a maximum distance the sealinggasket 904 can travel for, as predicted by a manufacturer of the sealinggasket 904). During the second operation phase 1206 the algorithm learnsa model of the sealing gasket 904 in unworn condition, where thewheel-related measurements (for example, a speed of the wheel 608, apressure of the wheel 608, etc.) are correlated to a measureddeformation of the magnetic field 934.

Once the model of the sealing gasket 904 in unworn condition is known,it is possible to compensate the deformation of the magnetic field 934due to external factors (such as the speed of the wheel 608, thepressure of the wheel 608, etc.) in a third operation phase 1208, thusobtaining a contribution of the deformation of the magnetic field 934due to the wear of the sealing gasket 904. The deformation of themagnetic field 934 due solely to a wear of the sealing gasket 904 may bereferred to as Δφ.

By monitoring Δφ across the space traveled by the rotary joint 903, itis possible to predict a remaining life of the sealing gasket 904 in afourth operation phase 1210. The estimation in the fourth operationphase 1210 is based on a regression of the latest values of Δφ andrelative distances covered (or operational time t) with a function ofknown shape (for example, polynomial or exponential) defined over: s→Δφ(or t→Δφ). The shape of the function can be determined based onexperimental tests performed in a laboratory setting. As an example, itcan be assumed that the function has an exponential shape of the form:Δφ_(f)(s)=Ae^(Bs), where A and B are the coefficients identified througha regression over the set of most recent measured magnetic fielddeformations Δφ and their corresponding traveled distances s. Thefunction Δφ_(f)(s) can be then used to predict what will be adeformation of the magnetic field Δφ if the distance s+Δs is traveled.

The condition of the sealing gasket 904 in a worn condition can beassociated to low values of Δφ_(f)(s) (where ‘low’ is a value calibratedfrom experimental tests performed in a laboratory setting). Anothersolution can be to monitor the speed at which Δφ_(f)(s) (or Δφ_(f)(t))is changing over a time period, defined as the derivative of Δφ_(f)(s)with respect to the time:

$\frac{d\; \Delta \; {\phi_{f}\left( {s(t)} \right)}}{dt} = \frac{{AB}\; e^{B\; s}}{v(t)}$

Where v(t) is an imposed speed profile of the wheel 608, based asinstance on a latest duty cycle performed by the vehicle including thedriveline 600 (shown in FIG. 6A). It should be noted that a dependencyof all the measurements with respect to time has been explicated. If thederivative of Δφ_(f)(s) with respect to the time is high (for examplegreater than an empiric threshold), the sealing gasket 904 can bedetermined as being worn and a feedback is provided to the user.Further, it is understood that another metric can be drawn fromΔφ_(f)(s) to identify a robust indicator.

The invention also comprises a method for cooperative mutual calibrationof the magnetic sensor 930 for wear monitoring of the sealing gaskets904. Measurements carried out by the magnetic sensors 930 are affectedby uncertainty and noise due to several factors (such as limitations intechnology, electro-magnetic noises from an environment, mountingerrors, and thermal drift, for example). It is possible to fuse suchinformation across all of the magnetic sensors 930 in order to increasethe accuracy of the model used to predict the residual life of thesealing gaskets 904 in a manner similar to the method describedhereinabove for automatic calibration of the magnetic sensors 930. It isunderstood that any sensor fusion technique could be utilized for such apurpose. An algorithm could be based on merging the data collected fromall the magnetic sensors 930. Such an extended dataset could then beutilized for estimating the wear model as described hereinabove formonitoring Δφ across the space traveled (or operating time) by therotary joint 903 for the automatic calibration of the magnetic sensors.

FIG. 11 illustrates the use of high pressure air within the rotary joint103, between the non-rotating portion 112 and the rotating portion 114,to apply a sealing force to each of the sealing gaskets 104. The sealingforce ensures that a gap between each of the sealing gaskets 104 and atleast one of the non-rotating portion 112 and the rotating portion 114is minimized. The use of high pressure air in this manner ensureselectrical contact is maintained between the electrodes 120 or thesemicircular electrodes 124 and the conductive material 122 shown inFIGS. 7A, 7B, 8A, and 8B.

In use, a wear of the sealing gaskets 104 or the dirt excluder 105 maybe determined by measuring an electrical signal at the pull-up digitalinput of the data logger. As a non-limiting example, when the sealinggasket 104 or the dirt excluder 105 is in a relatively new condition thedata logger reads a voltage close to a supplied voltage and when thesealing gasket 104 or the dirt excluder 105 is in a worn condition thedata logger reads a voltage close to zero.

Use of a single data logger in communication with the communication bus110 may be used to acquire data for at least all of the sensors 102fitted on one of the wheel hubs 100. It is understood that the datalogger may also be configured to acquire data for more than one wheelhub. As a non-limiting example, the data logger may be placed on anaxle, adjacent a differential, of the vehicle including the CTIS. It isunderstood however, that a location of the data logger may beapplication dependent, and may take into consideration many factors,such as but not limited to, protection of the data logger from mud orwater, for example. The data logger may be an industrial microcontrollerwhich acquires signals from the sensors 102 fitted on the wheel hubs100, processes the data according to at least one of methodologiespreviously described, and outputs a result to a vehicle CAN BUS (notshown). Such data can be utilized by a vehicle ECU for a wide range oftasks, such as but not limited to, vehicle diagnostics, vehicleprognostics, or control algorithms, for example. Each of the wheel hubs100 is interfaced to the data logger through a dedicated bus (notshown).

In addition to the above described embodiment, it is understood that thefollowing variations are also within the scope of the improvements forthe rotary joint 103 described herein. FIG. 13 is a chart indicatingwhich functionality described hereinabove relating to the methodologyfor rotary joint 103 and monitoring solutions for sealing gaskets areapplicable to two variations of a network of sensorized components for adriveline 600.

In a first variation, the sensors 102 may only be installed on one wheelhub 100. Such a configuration provides information on wear of at leastone of the sealing gaskets 104 and the dirt excluder 105 for a singlerotary joint 103. A wear of at least one of the sealing gaskets 104 andthe dirt excluder 105 of a remaining number of rotary joints 103 is thenpredicted based on the available information. In such a configuration,the encoder 116 on the rotary joint 103 only provides partialinformation regarding a traction torque delivered to the wheels 106 ofthe CTIS including the wheel hub 100. Accordingly, the benefits of thefirst variation are only to observe wear of at least one of the sealinggaskets 104 and the dirt excluder 105 used in the rotary joint 103.

In a second variation, the sensors 102 may be installed on at least two,but not all of the wheel hubs 100 of the vehicle. Such a configurationprovides information about the wear of at least one of the sealinggaskets 104 and the dirt excluder 105 in each of the configured rotaryjoint 103, and therefore it is possible to have better predictions onthe wear of at least one of the sealing gaskets 104 and the dirtexcluder 105 in any rotary joints 103 not fitted with sensors 102. Insuch a configuration, the encoder 116 on each of the rotary joints 103only provides partial information regarding a traction torque deliveredto the wheels 106 of the CTIS including the wheel hub 100. Accordingly,the benefits of the second variation are only to observe wear of atleast one of the sealing gaskets 104 and the dirt excluder 105 used ineach of the rotary joint 103.

In a third variation, the sensors 102 are installed on every wheel hub100 of the vehicle. Such a configuration monitors a status of each of atleast one of the sealing gaskets 104 and the dirt excluder 105 of all ofthe rotary joints 103, with no need to perform approximate predictionson the wear of the sealing gaskets 104 or the dirt excluder 105. Thethird variation provides a complete set of measurements of tractiontorques delivered to each of the wheels 106, thus allowing advancedvehicle dynamics control algorithms to be employed, as well as anestimation of the total thrusting force (Ft) of the vehicle. The totalthrusting force (Ft) of the vehicle may be calculated using thefollowing equation:

$F_{t} = {\sum\limits_{i = 0}^{n}{r_{i}T_{t,i}}}$

In the above equation, n is the number of wheel hubs 100 of the vehicle,while T_(t,i) and a radius n are a calculated traction torque and radiusof the wheel 106 designated by i. The benefits of the third variationare to observe wear of at least one of the sealing gaskets 104 and thedirt excluder 105 used in the rotary joint 103, observe rotational speedof the wheel 106, and observe delivered traction torque.

The drivetrain 600 for a vehicle, schematically illustrated in FIG. 6A,typically includes more than one axle 602 and differential 604. For sucha system, a torsional rigidity K may be difficult to determinebeforehand. However, the torsional rigidity K can be determined indedicated experimental tests in which the following are measured: arotational speed measured by a transmission speed sensor 605 at theoutput of a transmission 606 (which is driven by an engine 607), arotational speed of an associated wheel 608 including the rotary joint103, and an amount of torque delivered by the transmission 606. It isunderstood that the transmission speed sensor 605 is in communicationwith the communication bus 110.

An amount of traction torque delivered to each of the wheels 106 may beestimated from the measurements of the rotational speed of each of thewheels 106 and a rotational speed of a transmission 606 of the vehicleincluding the CTIS. The rotational speed of each of the wheels 106 isacquired using the above-described encoder 116 and method, while therotational speed of a transmission 606 is acquired through the use ofthe transmission speed sensor 605, which may be an encoder mounted on orwithin the transmission 606. As a non-limiting example, the transmissionspeed sensor 605 used to measure the rotational speed of thetransmission 606 may be a phonic wheel.

A methodology for determining the amount of traction torque is based ontorsional rigidity. FIG. 6B illustrates a shaft having first and secondends, which can be considered as a drive shaft for the vehicle. As aresult of torque applied by the transmission 606 to the first end, theshaft rotates to a degree, indicated by the angle φd. At the second end,an output torque will be equal to the input torque by the transmission606, however, a rotation angle φw will be reduced by a quantity Δφ. Thisdifference is due to the torsional stiffness K of the shaft. As K can bedetermined experimentally beforehand, and φd and φw can be measured, thetorque applied to the shaft can be calculated using the followingequation:

T=KΔϕ=K(ϕ_(d)−ϕ_(w))

In non-theoretical situations, however, drivelines such as thedrivetrain 600 are subjected to non-linear disturbances that cancompromise a measurement of angle phases φd and φw between a pair ofangular sensors. As a result, the estimation of traction torque for thedrivetrain 600 is also compromised. One non-linear disturbance is knownas driveline backlash. Driveline backlash may be defined as clearance orlost motion in a mechanism which is caused by gaps between twoconsecutive moving parts. Non limiting examples of affected moving partsare driveshaft joints and gear meshes. Such disturbances which arecategorized as drivelines backlash can also increase during theoperation of the drivetrain 600 of the vehicle.

To compensate for such non-linear disturbances, the invention alsoprovides a filtering algorithm that increases accuracy of the estimationof traction torque by selectively rejecting measurements which aredetermined to be affected by driveline backlash or other non-lineardisturbances. The filtering algorithm determines which measurements areaffected by driveline backlash or other non-linear disturbances byemploying artificial intelligence.

The filtering algorithm analyzes at least one of the following sourcesof information: a rotational speed of the transmission 606, a rotationalspeed of one or more of the wheels 608 measured at the rotary joint 103,and inputs from a driver of the vehicle the drivetrain 600 isincorporated in. As non-limiting examples, such inputs from the drivemay comprise a direction request (such as forward, neutral, and reverse,for example), a throttle position, a brake usage, a steering angle ofthe vehicle, and a movement of auxiliary working implements (such astelescopic boom, for example).

Such information analyzed by the filtering algorithm information can belogged by a plurality of sensors placed to collect such information atthe same time, but it is also understood that such information can becollected at different time instants if a timestamp is associated toeach data. It is understood that at least a portion of such informationmay be collected by the sensors 102, 116. A remaining portion of suchinformation may be collected or inferred by a plurality of sensors orinformation available via the communication bus 110. The collectedinformation is then fed into a sensor fusion and processing layer (SFP)of the data logger or a controller in communication with thecommunication bus 110, where the complete set (or a reduced set) ofmeasurements is merged in order to reconstruct a current operationalstate of the vehicle including the drivetrain 600. FIG. 6C schematicallyillustrates such a process. Further, FIGS. 6D, 6F, and 12 alsoschematically illustrate similar filtering processes. As shown in FIG.6C, the information is merged in the SFP depending on a number ofsources of observable information available to the SFP. In generalhowever, the greater a number of sources of observable information thatare available to the SFP, the more accurate will be an estimation of thestate of the vehicle including the drivetrain 600.

For instance, the heterogeneous set of data measured at a given timeinstant t_(k) can be collected into an array of measurements m(t_(k)).The SFP layer then combines the current m(t_(k)) together with itsprevious values (for example, the history of m(t)) in order to extractrelevant information and structured data. The latter can be forinstance, but not limited to, a transformation of one or more signals inm(t_(k)) with a Fourier or Wavelet transformation (or any other suitablestatistical descriptor of time or frequency properties of the signalm(t_(k))). The processed information is stored in an array of featuresf(t_(k)).

At least a portion of the filtering algorithm resides in aclassification layer. In the classification layer, the most recent arrayof features f(t_(k)) is compared against a previously learned model ofthe vehicle (LMV) to decide whether a current vehicle state is free fromundesired disturbances (such as backlash). The traction torque isestimated if the latter condition is true, otherwise the traction torqueis not updated (alternately, the traction torque can also be set equalto zero). This procedure is performed for each of the measured wheels,and any classification algorithm can be used for this purpose (asnon-limiting examples, logistic regression, support vector machines, ordeep learning may be used as a classification algorithm).

In the process that utilizes the filtering algorithm that increasesaccuracy of the estimation of traction torque, a very important part isrepresented by the Learned Model of the Vehicle, against which computedfeature arrays f(t_(k)) are classified. The LMV can be represented forinstance as a set of f(t) arrays (each having the same mathematical andphysical properties as f(t_(k))) that are defined over an N-dimensionalspace (for example, the same dimension of f(t_(k))). One out of twolabels is assigned to each array of LMV. As a non-limiting example, apositive label may be assigned for arrays of features which are notaffected by disturbances, and a negative label may be assigned forarrays of features that are affected by disturbances.

The LMV is initialized with a first calibration phase that is performedonly once. During the calibration phase, various measurement arrays m(t)are collected (and the respective features arrays f(t) are calculated)while performing several maneuvers that are characterized by the absenceand presence of external disturbances (for example, acceleration of thevehicle, travel at a substantially constant speed, and an inversion ofvehicle movements, among other maneuvers). A skilled user, such as atechnician, then associates to each feature array f(t) a positive labelif it is associated, to a measurement array that is not affected bydisturbances, and a negative label if it associated to a measurementarray that is affected by disturbances (such as, but not limited tobacklash, for example).

The filtering algorithm that increases accuracy of the estimation oftraction torque provides a methodology that can adapt to mutatingoperating conditions, which are very common for off-highway vehicles,which encounter a wide range of different operating conditions. Toovercome this challenge, the invention also comprises a learningalgorithm for determining how the vehicle is being used in order toimprove the accuracy of a classification (for example, theidentification of measurement data that is not affected bydisturbances). Having a learning classification layer means that theLearned Model of the Vehicle is updated based on new operatingconditions. Any suitable machine learning algorithm can be used toupdate the LMV (such as but not limited to, neural networks, deeplearning, Gaussian processes, support vector machines, for example).FIG. 6D schematically illustrates the learning algorithm. Once a currentf(t_(k)) has been calculated, it is calculated the distance of thisarray of feature with respect to the current LMV. The distance can bequantified by any suitable metrics (such as the Mahalanobis distance,for example). If the calculated distance is greater than a predeterminedthreshold, the array of feature f_(LMV,distmin)(t) in LMV that is closerto f(t_(k)) is updated. The update operation can be a replacement off_(LMV,distmin)(t) with f(t_(k)) in order to have an aggressive or quicklearning process. If a less aggressive learning is desired,f_(LMV,distmin)(t) can be replaced with an intermediate array betweenf_(LMV,distmin)(t) and f(t_(k)). It is understood, however, that such anintermediate array could be calculated through any suitable method, suchas a weighted average, for example.

The aggressiveness of learning should be selected according to eachspecific application and in any case by looking for a compromise betweenan increase of robustness with respect to varying operating conditionsand the increase of computational resources required for updating theclassification algorithm based on the updated LMV. In general, vehiclesused in short and intermittent duty cycles should update (learn) in anonline manner the LMV set of array of features less frequently, sincethe operating conditions will likely change less frequently. In such acase, the torque estimation will be made more often and during shortacceleration phase since a control unit will not be busy in updating theLMV set. On contrary, vehicles with long duty cycles will benefit frommore frequent updates of LMV as long as their operating conditionscontinue to change. It is also possible to calibrate the aggressivenessof the learning automatically, through a procedure that monitors thelatest performed duty cycles, and classifies them “short” or “long” asinstance based on a traveled distance, or based on a more complicatedset of features.

The invention also provides a method to facilitate determining anefficiency of the transmission 606. By knowing the traction torque inaccordance with the methods described hereinabove at a given wheel 608and a rotational speed of the given wheel 608, it is possible tocalculate an amount of power delivered at the wheel 608. Next, theefficiency of the transmission 606 is computed as the ratio between thesum of all the powers of the wheels 608 and the power delivered by theengine 607, which is information that is available via the communicationbus 110.

The invention also provides a method to determine a health status of thedrivetrain 600 for a vehicle using the measurements of the speed of thetransmission 606 and all of the wheels 608. A set of heuristic rules areused to determine a functional status of the plurality of sensors 116and at least one sensor used with the transmission 606. As non-limitingexamples, the invention comprises the following approaches todetermining a health status of the drivetrain 600; determining a healthstatus of the sensor 116 used to determine a speed of the wheel 608 anddetermining a health status of a traction speed sensor.

In determining a health status of the sensor 116 used to determine aspeed of the wheel 608, it is assumed that the vehicle includes fourwheels 608. In the event that non-null velocities are read at thetransmission speed sensor 605 and at all the wheel speed sensors 116 butone, it is then necessary to identify whether the zero wheel speedmeasurement is due to a malfunctioning wheel speed sensor 116 or to alocked wheel. Since vehicles typically have a, differential, such as thedifferential 604, connecting the wheels 608 on the same axle, anon-rotating locked wheel is a condition that might happen during thenormal operation of the vehicle, without implicating that the wheelsensor speed has failed. Usually, in the normal use of the vehicle alocked wheel is a condition that will only last shortly since anoperator of the vehicle will notice such an event and try to compensatefor it, such as by reducing the throttle. Further, such an event impliesthat the traction power is not being delivered at all the four wheels608, but only at the wheels 608 that are rotating. Therefore, a zerospeed measurement at one wheel 608 is associated with a wheel 608 in alocked condition if the traction power at the remaining wheels 608increases. Further, a variation of this approach can include themeasurement of the power at the engine 607, which is used as anadditional verification, since a total amount of power transmitted atthe wheels 608 is proportional to the power delivered by the engine 607(wherein the proportional coefficient is the efficiency of the driveline600). Alternately, if a zero speed reading is not associated to anincreased amount of traction power at the remaining wheels 608, it isindicative that all the wheels 608 are rotating and that the sensor 116associated with the zero-reading may be non-operational.

A health status of the transmission speed sensor 605, which is used todetermine traction speed, can be determined by monitoring the speed ofall the wheels 608 on at least one driving axle 602 (for example, allthe wheels 608 on the front axle or on the rear axle, in the case ofall-wheel drive drivelines). In the event that, both the wheels on thesame axle 602 are spinning and the transmission speed sensor 605 isreading a zero speed, it can be inferred that the transmission speedsensor 605 is non-operational.

The invention also provides a method to facilitate adaptive learning ofvehicle tractive performance. A tractive performance of the vehicleincluding the driveline 600 might deteriorate due to a progressive wearof mechanical components of the axles 602 (such as but not limited tobearings, gears, etc.) and to a deterioration of a lubrication oil.These phenomena can be captured by monitoring the efficiency of the axle602 according to the procedure described hereinabove.

By monitoring the efficiency of the axle 602 with the method tofacilitate determining efficiency, it is possible to determine whetherthe oil is deteriorated or the mechanical components of the axle 602 areworn. The method to facilitate adaptive learning of vehicle tractiveperformance is based on the concept that the efficiency of the axle 602tends to decrease over a service life of the vehicle. However, whenregular maintenance is performed on the axle 602 (such as but notlimited to an oil of the axle 602 being changed), such maintenanceallows a recuperation of a portion of the lost efficiency. FIG. 6E is achart which illustrates a deterioration of the efficiency of thedriveline 600 with respect to a number of operating hours of thedriveline 600. As shown in FIG. 6E, maintenance performed at periodintervals can recuperate a portion of efficiency lost over the servicelife. FIG. 6E shows the efficiency with respect to a number of operatinghours of the driveline 600, but it is understood that efficiency may bemeasured with respect to a distance traveled by the vehicle includingthe driveline 600 or 10, other variables.

FIG. 6E illustrates the trend of the driveline efficiency μ with respectto the operating hours of the vehicle including the driveline 600. μ0sis the value of the efficiency when the vehicle is in a new condition,μ0f is the value of the efficiency preceding a first maintenance, andμ1s is the value of the efficiency following the first maintenance. Suchnotation may be extended to additional maintenance periods as shown inFIG. 6E. After the first maintenance, μ1s will be greater than μ0f,which is primarily the result of a lubricant used with the axle 602being replaced.

Accordingly, by monitoring an increase in efficiency before and after amaintenance (for example, the difference between μ1s and μ0f), it ispossible to estimate in an online manner (meaning during a lifecycle ofthe vehicle) a rate of driveline efficiency drop due to oildeterioration, which may be indicated as r_(μ,oil).

A periodic maintenance of the axle 602 allows for recuperation of aportion of driveline efficiency lost due to oil deterioration, and not aportion of efficiency lost due to wear of mechanical components, andtherefore μ1s will be smaller than μ0s. By considering the efficienciesmeasured between two consecutive periodic maintenances (for example, thedifference between μ1s−μ0s) it is possible to calculate a rate ofefficiency drop due to worn mechanical components r_(μ,wear). The rater_(μ,wear) is typically smaller than r_(μ,oil) since mechanicalcomponents used with the axle 602 are designed for long lifecycles.

The rates r_(μ,oil) and r_(μ,wear) will vary over time, since the twowear phenomena are not linear, and typically are correlated. Forinstance, deteriorated lubrication oil will increase a wear ofmechanical components. For this reason, among others, the two rates canbe computed each time maintenance is performed on the vehicle.

The method to facilitate adaptive learning of vehicle tractiveperformance has many interesting consequences. First, the continuouslyupdated values of r_(μ,oil) and r_(μ,wear) allow to have a robustprediction of a rate of reduction of the total efficiency, even when thevehicle is used on a wide range of conditions. Next, the prediction ofthe residual driveline efficiency can be used to perform an optimal planof maintenance sessions. Lastly, a manufacturer of the axle 602 canmerge the estimated efficiencies from different vehicles having similaraxles, thus reconstructing a reliable empirical model that can be usedto predict a performance and lifecycle of the axle.

The invention also provides a method to facilitate an adaptive diagnosisof a health status of the driveline 600. Regarding the above describedmethod to method to facilitate adaptive learning of vehicle tractiveperformance, such a method focuses on wear mechanisms with a slowdynamic. The method to facilitate adaptive diagnosis of a health statusof the driveline 600 however, handles wear mechanisms with a fastdynamic. Such a method covers numerous phenomena relating to themechanical components of the driveline 600. Non-limiting examples ofthese phenomena are broken teeth of gears used in the driveline 600,damaged races of bearings, or fretting. Further, any mechanismcharacterized by a periodic pattern as a result of a worn component canbe covered by the method.

The adaptive diagnosis is based an intelligent algorithm that identifiesanomalous patterns in the signals measured at the driveline 600. Themeasurements of these signals can be, for example, a rotational speed atone or more of the wheels 608, a rotational speed at the transmission606, an estimated traction torque, an estimated efficiency of thedriveline 600. Further, it is understood that any combination of thisinformation may be utilized by the adaptive diagnosis. Theidentification of anomalous patterns utilizes the same algorithm shownin FIG. 6D, with the exception of the step for updating the LearnedModel of the Vehicle, which in this case is called a Learned Model ofthe Driveline (LMD), and is schematically illustrated in FIG. 6F. As anon-limiting example, the measurement vector might include the estimatedtraction torque, from which it is calculated the feature vector f(t) byusing a Fast Fourier Transform or a Wavelet. However, it is understoodthat any other meaningful transformation might also be suitable.

The LMD can be represented as a set of feature vectors generated fromtheoretical models of different damage modes. Such theoretical modelsare evaluated for the current measurement data, in order to adapt theLMD to the current operating condition of the driveline 600. As shown inFIG. 6G, a classifier then searches if a measured feature vector f_(tk)contains any known pattern in the LMD. If so, it is identified whichdamage mode has been found, based on the comparison. As shown in FIG.6G, which is exemplary, the known pattern identified by the classifieris indicated as identified damage D2.

In addition to the above described embodiment, it is understood that thefollowing variations are also within the scope of the methodologiesrelated to determining the amount of traction torque for the driveline600 described herein. FIG. 13 is a chart indicating which functionalitydescribed hereinabove relating to the methodology for determining theamount of traction torque for the driveline 600 is applicable to fourvariations of a network of sensorized components for a driveline 600.

In a first variation, the encoder 116 is installed on only one wheel hub100 and the speed of the transmission 606 is measured using thetransmission speed sensor 605. In such a configuration, the encoder 116of the rotary joint 103 which is sensorized provide only partialinformation on the traction torque of the wheels 608 of the vehicle thedriveline 600 is incorporates in. Accordingly, such a variation does notprovide enough data to be used with some of the vehicle dynamic controlalgorithms described herein. However, using such a variation it is stillpossible to monitor the health status of the driveline 600 by lookingfor anomalies in the recorded speeds of the wheel hub 100 andtransmission 606, as described hereinabove.

In a second variation, the encoder 116 is installed on all the wheelhubs 100 of a drive axle (such as when only one of the axles 602 isdriven by the transmission 606) and the speed of the transmission 606 ismeasured using the transmission speed sensor 605. In addition to thebenefits of the first variation described above, it is also possible toimplement the method to facilitate an adaptive diagnosis of a healthstatus of the driveline 600 as described hereinabove.

In a third variation, the encoder 116 is installed on all the wheel hubs100 of all but one of the axles 602 and the speed of the transmission606 is measured using the transmission speed sensor 605. With thisconfiguration it is possible to measure the traction torque on all thewheel hubs 100 that include encoders 116, and then estimate the tractiontorque at the axle 602 having wheel hubs 100 that do not includeencoders 116. The traction torque at the axle 602 having wheel hubs 100that do not include encoders 116 is the difference between the torquegenerated by the engine 607 at a flywheel (not shown) (and multipliedfor ratio of the transmission 606), and the sum of all the estimatedtraction torques at the wheel hubs 100 that include encoders 116. Itshould be noted that the traction torque estimated at the axle 602having wheel hubs 100 that do not include encoders 116 may be lessaccurate than using other methods, as the efficiency of the driveline600 cannot be estimated using the third variation.

In a fourth variation, the encoder 116 is installed on all the wheelhubs 100 of all the axles 602 and the speed of the transmission 606 ismeasured using the transmission speed sensor 605. Out of all of thevariations described herein, this variation is the most completeconfiguration, and allows all the proposed methodologies related to thetorque estimation to be performed.

Further, it is understood that any permutation of the first throughfourth variants described hereinabove may be combined with any of thevariants relating to the rotary joint 103 described hereinabove.

In accordance with the provisions of the patent statutes, the presentinvention has been described in what is considered to represent itspreferred embodiments, however, it should be noted that the inventioncan be practiced otherwise than as specifically illustrated anddescribed without departing from its scope or spirit.

What is claimed is:
 1. A network of sensorized components for adriveline, comprising: a sensorized wheel hub in communication with acommunication bus; a transmission speed sensor in communication with thecommunication bus; and a controller in communication with at least oneof the sensorized wheel hub and the transmission speed sensor throughthe communication bus, wherein the controller facilitates determining atleast one of: an amount of torque applied to at least one wheel forminga portion of the driveline, an estimated efficiency of a transmission ofthe driveline, an operational status of at least one of the sensorizedwheel hub and the transmission speed sensor, an efficiency of thedriveline, and a status of a portion of the driveline using signalanalysis.
 2. The network according to claim 1, wherein the controllerfacilitates determining the amount of torque applied to at least onewheel forming a portion of the driveline after analyzing informationcollected from the sensorized wheel hub and the transmission speedsensor and comparing the information collected to a learned model. 3.The network according to claim 1, wherein the controller facilitatesdetermining the estimated efficiency of a transmission of the drivelineby comparing the amount of torque applied to at least one wheel forminga portion of the driveline and an amount of power delivered by anengine.
 4. The network according to claim 1, wherein the controllerfacilitates determining an operational status of the sensorized wheelhub by comparing information from a wheel speed sensor of a first wheelto an amount of power delivered to a second wheel.
 5. The networkaccording to claim 1, wherein the controller facilitates determining anoperational status of the transmission speed sensor by comparinginformation from the transmission speed sensor to information from awheel speed sensor located on a drive axle of the driveline.
 6. Thenetwork according to claim 1, wherein the controller facilitatesdetermining an efficiency of the driveline based on an efficiencydeterioration model of the driveline and a periodic maintenance of thedriveline.
 7. The network according to claim 1, wherein the controllerfacilitates determining a status of a portion of the driveline usingsignal analysis and identification of anomalous patterns indicative of adamaged component of the driveline.
 8. A method of utilizing a networkof sensorized components to analyze a driveline, comprising the stepsof: providing a sensorized wheel hub in communication with acommunication bus; providing a transmission speed sensor incommunication with the communication bus; providing a controller incommunication with the sensorized wheel hub and the transmission speedsensor through the communication bus; collecting information from thesensorized wheel hub and the transmission speed sensor; fusing thecollected information using the controller to reconstruct a currentoperational state of the driveline; comparing a previously learned modelof the driveline to the current operational state of the driveline; anddetermining if the current operational state of the driveline is freefrom undesired disturbances, wherein in the event that the driveline isfree from undesired disturbances an estimation of the driveline torqueis performed.
 9. The method according to claim 8, further comprising thestep of determining an estimated efficiency of a transmission of thedriveline by comparing the amount of torque applied to at least onewheel forming a portion of the driveline and an amount of powerdelivered by an engine.
 10. The method according to claim 8, furthercomprising the step of determining an operational status of thesensorized wheel hub by comparing information from a wheel speed sensorof a first wheel to an amount of power delivered to a second wheel. 11.The method according to claim 8, further comprising the step ofdetermining an operational status of the transmission speed sensor bycomparing information from the transmission speed sensor to informationfrom a wheel speed sensor located on a drive axle of the driveline. 12.The method according to claim 8, further comprising the step ofdetermining a decreased efficiency of the driveline based on anefficiency deterioration model of the driveline and a periodicmaintenance of the driveline.
 13. The method according to claim 8,further comprising the step of determining a status of a portion of thedriveline using signal analysis and identification of anomalous patternsindicative of a damaged component of the driveline.
 14. A method ofutilizing a network of sensorized components to analyze a driveline,comprising the steps of: providing a communication bus; providing asensorized wheel hub in communication with the communication bus;providing a transmission speed sensor in communication with thecommunication bus; providing a controller in communication with at leastone of the sensorized wheel hub and the transmission speed sensor;collecting information from at least one of the sensorized wheel hub andthe transmission speed sensor; and processing the information from atleast one of the sensorized wheel hub and the transmission speed sensorusing the controller to determine an operational state of the driveline.15. The method according to claim 14, further comprising the step ofdetermining whether the operational state of the driveline includes atleast one disturbance.
 16. The method according to claim 15, furthercomprising the step of estimating a driveline torque based upon theoperational state of the driveline.